Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f331105b048>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f3310f95b00>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    

    
    # Discriminator Inputs
    real_inputs = tf.placeholder(
                                dtype = tf.float32,
                                shape = [None, image_height, image_width, image_channels],
                                name = 'real_inputs'
                                )
    # Generator Inputs
    z_inputs = tf.placeholder(
                              dtype = tf.float32,
                              shape = [None, z_dim],
                              name = 'z_inputs'
                              )
    
    # learning Rate scalar
    learning_rate = tf.placeholder(
                                   dtype = tf.float32,
                                   shape = None,
                                   name = 'learning_rate'
                                  ) 

    return (real_inputs,z_inputs,learning_rate)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
# set leaky parameter
alpha = 0.2


# create a Leaky RelU activation function 
# reference: 'Hands-On Machine Learning with Scikit-Learn and TensorFlow' by Aurélien Géron
def LeakyRELU(input, name = None):
    return tf.maximum(alpha*input,input,name = name)
In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    # kernel_initializer set-up stddev based on RGB or grayscale
    sigma_init = 0.1 if images.shape[3] == 1 else 0.03
    
    with tf.variable_scope('discriminator', reuse = reuse):
        
        
        # 1st Convolutional Layer w/ LeakyReLU activation and w/o batch norm
        Lrelu1 = tf.layers.conv2d(
                                  inputs = images,
                                  filters = 64,
                                  kernel_size = (3,3),
                                  strides=(2,2),
                                  padding='same',
                                  kernel_initializer=tf.random_normal_initializer(mean = 0.0, stddev=sigma_init),
                                  activation = LeakyRELU
                                  )
     
        
        # 2nd Convolutional Layer w/ LeakyReLU activation and batch norm
        Lrelu2 = tf.layers.conv2d(
                                  inputs = Lrelu1,
                                  filters = 128,
                                  kernel_size = (3,3),
                                  strides=(2,2),
                                  padding='same',
                                  kernel_initializer=tf.random_normal_initializer(mean = 0.0, stddev=sigma_init),
                                  activation = None
                                  )
        Lrelu2 = tf.layers.batch_normalization(Lrelu2, training=True)
        Lrelu2 = LeakyRELU(Lrelu2) #note: have to apply LeakyRELU after batch norm
        
        
        # 3rd Convolutional Layer w/ LeakyReLU activation and batch norm
        Lrelu3 = tf.layers.conv2d(
                                  inputs = Lrelu2,
                                  filters = 256,
                                  kernel_size = (3,3),
                                  strides=(2,2),
                                  padding='same',
                                  kernel_initializer=tf.random_normal_initializer(mean = 0.0, stddev=sigma_init),
                                  activation = None
                                  )
        Lrelu3 = tf.layers.batch_normalization(Lrelu3, training=True)
        Lrelu3 = LeakyRELU(Lrelu3)
        
        
        # 4th Convolutional Layer w/ LeakyReLU activation and batch norm
        Lrelu4 = tf.layers.conv2d(
                                  inputs = Lrelu3,
                                  filters = 512,
                                  kernel_size = (3,3),
                                  strides=(2,2),
                                  padding='same',
                                  kernel_initializer=tf.random_normal_initializer(mean = 0.0, stddev=sigma_init),
                                  activation = None
                                  )
        Lrelu4 = tf.layers.batch_normalization(Lrelu4, training=True)
        Lrelu4 = LeakyRELU(Lrelu4)
        
  
        
        # Flattened Layer
        flat = tf.contrib.layers.flatten(Lrelu4)
        
        # Fully-Connected layer w/ one unit
        logits = tf.layers.dense(flat, 1)
        
        # Sigmoid Output
        out = tf.sigmoid(logits)
        
        
        #print('InputShape = ',images.shape)
        #print('Lrelu1_Shape = ',Lrelu1.shape)
        #print('Lrelu2_Shape = ',Lrelu2.shape)
        #print('Lrelu3_Shape = ',Lrelu3.shape)
        #print('Lrelu4_Shape = ',Lrelu4.shape)
 
    
    return (out, logits)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    # kernel_initializer set-up stddev based on RGB or grayscale
    sigma_init = 0.1 if out_channel_dim == 1 else 0.03

    with tf.variable_scope('generator', reuse=not(is_train)):
        # First fully connected layer
        connect_layer = tf.layers.dense(
                                        inputs = z, 
                                        units = 2*2*512,
                                        kernel_initializer=tf.truncated_normal_initializer(mean = 0.0, stddev=sigma_init)
                                        )
        # Reshape it to start the convolutional stack
        connect_layer = tf.reshape(connect_layer, (-1, 2, 2, 512))
        connect_layer = tf.layers.batch_normalization(connect_layer, training=is_train)
        Lrelu0 = LeakyRELU(connect_layer)
        
       
        # 1st 'Deconvolutional'-layer w/ batch normalization
        Lrelu1 = tf.layers.conv2d_transpose(
                                            inputs = Lrelu0, 
                                            filters = 256, 
                                            kernel_size = (3,3),
                                            strides= (2,2),
                                            padding='same',
                                            kernel_initializer=tf.truncated_normal_initializer(mean = 0.0, stddev=sigma_init),
                                            activation = None
                                            )        
        Lrelu1 = tf.layers.batch_normalization(Lrelu1, training=is_train)
        Lrelu1 = LeakyRELU(Lrelu1)
        

       
        # 2nd 'Deconvolutional'-layer w/ batch normalization
        Lrelu2 = tf.layers.conv2d_transpose(
                                            inputs = Lrelu1, 
                                            filters = 128, 
                                            kernel_size = (4,4),
                                            strides= (1,1),
                                            padding='valid',
                                            kernel_initializer=tf.truncated_normal_initializer(mean = 0.0, stddev=sigma_init),
                                            activation = None
                                            )  
        Lrelu2 = tf.layers.batch_normalization(Lrelu2, training=is_train)
        Lrelu2 = LeakyRELU(Lrelu2)
        
    
        # 3rd 'Deconvolutional'-layer w/ batch normalization
        Lrelu3 = tf.layers.conv2d_transpose(
                                            inputs = Lrelu2, 
                                            filters = 64, 
                                            kernel_size = (3,3),
                                            strides= (2,2),
                                            padding='same',
                                            kernel_initializer=tf.truncated_normal_initializer(mean = 0.0, stddev=sigma_init),
                                            activation = None
                                            )
        Lrelu3 = tf.layers.batch_normalization(Lrelu3, training=is_train)
        Lrelu3 = LeakyRELU(Lrelu3)
        
        
        # 4th 'Deconvolutional'-layer w/ batch normalization
        Lrelu4 = tf.layers.conv2d_transpose(
                                            inputs = Lrelu3, 
                                            filters = 32, 
                                            kernel_size = (5,5),
                                            strides= (1,1),
                                            padding='same',
                                            kernel_initializer=tf.truncated_normal_initializer(mean = 0.0, stddev=sigma_init),
                                            activation = None
                                            )
        Lrelu4 = tf.layers.batch_normalization(Lrelu4, training=is_train)
        Lrelu4 = LeakyRELU(Lrelu4)
        
        
        
        # Output layer w/ tanh activation [-1 1]
        out = tf.layers.conv2d_transpose(
                                            inputs = Lrelu4, 
                                            filters = out_channel_dim, 
                                            kernel_size = (3,3), 
                                            strides= (2,2), 
                                            padding='same',
                                            kernel_initializer=tf.truncated_normal_initializer(mean = 0.0, stddev=sigma_init),
                                            activation = tf.tanh
                                            )
        
        
        # 28x28xout_channel_dim now
        #print('Lrelu0_Shape = ',Lrelu0.shape)
        #print('Lrelu1_Shape = ',Lrelu1.shape)
        #print('Lrelu2_Shape = ',Lrelu2.shape)
        #print('Lrelu3_Shape = ',Lrelu3.shape)
        #print('logits_Shape = ',logits.shape)
       
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    # reference: https://github.com/udacity/deep-learning/blob/master/gan_mnist/Intro_to_GANs_Solution.ipynb
    
    g_model = generator(input_z, out_channel_dim, is_train=True)
   
    (d_model_real, d_logits_real) = discriminator(input_real, reuse = False)
    (d_model_fake, d_logits_fake) = discriminator(g_model, reuse=True)

    # parameter for label smoothing
    label_smooth = 0.05;
    
    # The losses are calculated by sigmoid cross-entropy and will take the mean of all images in a batch
    
    # label smoothing applied to real logits labels
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1-label_smooth)))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    # the generator losses use the fake image logits
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    # Discriminator total loss is the sum of the losses for real and fake images
    d_loss = d_loss_real + d_loss_fake

    return (d_loss, g_loss)
    
 

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # reference: https://github.com/udacity/deep-learning/blob/master/gan_mnist/Intro_to_GANs_Solution.ipynb
    
    # Get the trainable_variables split into Generator and Discriminator parts
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Adam Optimizer
    

    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    # Reference: https://github.com/udacity/deep-learning/blob/master/batch-norm/Batch_Normalization_Lesson.ipynb
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) 
    with tf.control_dependencies([op for op in update_ops if op.name.startswith('generator')]):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return (d_train_opt, g_train_opt)
    
    
 


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    #print(data_image_mode)
    #print(data_shape)
 
           
    image_channels = data_shape[3]
    #print(image_channels)
    
    (input_real, input_z, lr) = model_inputs(
                                            image_width = data_shape[1],
                                            image_height = data_shape[2],
                                            image_channels = image_channels,
                                            z_dim = z_dim
                                            )
    
    (d_loss, g_loss) = model_loss(input_real, input_z, image_channels)
    (d_train_opt, g_train_opt) = model_opt(d_loss, g_loss, learning_rate, beta1)
    

    (samples, losses) = [], []
    print_every = 100

    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0;
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                steps += 1
                
                # re-scale images from [-0.5 0.5] to [-1 1] to match tanh generator output
                batch_images *= 2

                
                # Sample random noise for Generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                
                # Run optimizers. 
                # Run g_optim multiple times to ensure that d_loss does not go to zero 
                # (Reference:https://github.com/carpedm20/DCGAN-tensorflow)
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, lr: learning_rate})

                
               
                
                if steps % print_every == 0:
                    show_generator_output(
                                          sess = sess,
                                          image_mode = data_image_mode,
                                          input_z = input_z,
                                          n_images = 25,
                                          out_channel_dim = image_channels
                                          )
                    # get the losses and print them out
                    train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                  
 

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [17]:
batch_size = 64
z_dim = 100
learning_rate = 0.0006
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 100 Discriminator Loss: 0.6581... Generator Loss: 1.5818
Epoch 1/2... Batch 200 Discriminator Loss: 0.7881... Generator Loss: 1.4275
Epoch 1/2... Batch 300 Discriminator Loss: 1.0240... Generator Loss: 1.2892
Epoch 1/2... Batch 400 Discriminator Loss: 1.3744... Generator Loss: 0.5091
Epoch 1/2... Batch 500 Discriminator Loss: 1.1843... Generator Loss: 0.6890
Epoch 1/2... Batch 600 Discriminator Loss: 1.2115... Generator Loss: 0.6712
Epoch 1/2... Batch 700 Discriminator Loss: 1.4424... Generator Loss: 1.4042
Epoch 1/2... Batch 800 Discriminator Loss: 1.4803... Generator Loss: 0.4273
Epoch 1/2... Batch 900 Discriminator Loss: 1.2163... Generator Loss: 0.9136
Epoch 2/2... Batch 100 Discriminator Loss: 1.6676... Generator Loss: 0.3220
Epoch 2/2... Batch 200 Discriminator Loss: 1.3592... Generator Loss: 0.6855
Epoch 2/2... Batch 300 Discriminator Loss: 1.0768... Generator Loss: 0.7913
Epoch 2/2... Batch 400 Discriminator Loss: 1.1938... Generator Loss: 0.6943
Epoch 2/2... Batch 500 Discriminator Loss: 1.5355... Generator Loss: 0.4344
Epoch 2/2... Batch 600 Discriminator Loss: 1.2495... Generator Loss: 0.9184
Epoch 2/2... Batch 700 Discriminator Loss: 1.2787... Generator Loss: 0.5596
Epoch 2/2... Batch 800 Discriminator Loss: 1.5283... Generator Loss: 0.3792
Epoch 2/2... Batch 900 Discriminator Loss: 1.4603... Generator Loss: 0.4014

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [16]:
batch_size = 32
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 100 Discriminator Loss: 1.7931... Generator Loss: 0.5096
Epoch 1/1... Batch 200 Discriminator Loss: 1.4020... Generator Loss: 0.6851
Epoch 1/1... Batch 300 Discriminator Loss: 1.4644... Generator Loss: 0.6712
Epoch 1/1... Batch 400 Discriminator Loss: 1.6020... Generator Loss: 0.6215
Epoch 1/1... Batch 500 Discriminator Loss: 1.3996... Generator Loss: 0.7152
Epoch 1/1... Batch 600 Discriminator Loss: 1.5076... Generator Loss: 0.6783
Epoch 1/1... Batch 700 Discriminator Loss: 1.3894... Generator Loss: 0.7239
Epoch 1/1... Batch 800 Discriminator Loss: 1.4273... Generator Loss: 0.7032
Epoch 1/1... Batch 900 Discriminator Loss: 1.4346... Generator Loss: 0.6674
Epoch 1/1... Batch 1000 Discriminator Loss: 1.4436... Generator Loss: 0.6666
Epoch 1/1... Batch 1100 Discriminator Loss: 1.4190... Generator Loss: 0.7319
Epoch 1/1... Batch 1200 Discriminator Loss: 1.4501... Generator Loss: 0.6978
Epoch 1/1... Batch 1300 Discriminator Loss: 1.4160... Generator Loss: 0.6995
Epoch 1/1... Batch 1400 Discriminator Loss: 1.3768... Generator Loss: 0.7237
Epoch 1/1... Batch 1500 Discriminator Loss: 1.3945... Generator Loss: 0.6774
Epoch 1/1... Batch 1600 Discriminator Loss: 1.3859... Generator Loss: 0.6888
Epoch 1/1... Batch 1700 Discriminator Loss: 1.4346... Generator Loss: 0.6941
Epoch 1/1... Batch 1800 Discriminator Loss: 1.3906... Generator Loss: 0.7267
Epoch 1/1... Batch 1900 Discriminator Loss: 1.4427... Generator Loss: 0.7087
Epoch 1/1... Batch 2000 Discriminator Loss: 1.4329... Generator Loss: 0.7066
Epoch 1/1... Batch 2100 Discriminator Loss: 1.3792... Generator Loss: 0.7835
Epoch 1/1... Batch 2200 Discriminator Loss: 1.4325... Generator Loss: 0.6793
Epoch 1/1... Batch 2300 Discriminator Loss: 1.4450... Generator Loss: 0.6666
Epoch 1/1... Batch 2400 Discriminator Loss: 1.4126... Generator Loss: 0.6960
Epoch 1/1... Batch 2500 Discriminator Loss: 1.3850... Generator Loss: 0.7548
Epoch 1/1... Batch 2600 Discriminator Loss: 1.3829... Generator Loss: 0.7120
Epoch 1/1... Batch 2700 Discriminator Loss: 1.3961... Generator Loss: 0.7225
Epoch 1/1... Batch 2800 Discriminator Loss: 1.4302... Generator Loss: 0.7021
Epoch 1/1... Batch 2900 Discriminator Loss: 1.3967... Generator Loss: 0.6950
Epoch 1/1... Batch 3000 Discriminator Loss: 1.4181... Generator Loss: 0.6737
Epoch 1/1... Batch 3100 Discriminator Loss: 1.3982... Generator Loss: 0.7690
Epoch 1/1... Batch 3200 Discriminator Loss: 1.4087... Generator Loss: 0.6938
Epoch 1/1... Batch 3300 Discriminator Loss: 1.4169... Generator Loss: 0.6577
Epoch 1/1... Batch 3400 Discriminator Loss: 1.3887... Generator Loss: 0.7081
Epoch 1/1... Batch 3500 Discriminator Loss: 1.4097... Generator Loss: 0.6963
Epoch 1/1... Batch 3600 Discriminator Loss: 1.3853... Generator Loss: 0.7088
Epoch 1/1... Batch 3700 Discriminator Loss: 1.4118... Generator Loss: 0.7204
Epoch 1/1... Batch 3800 Discriminator Loss: 1.4107... Generator Loss: 0.7743
Epoch 1/1... Batch 3900 Discriminator Loss: 1.4016... Generator Loss: 0.7495
Epoch 1/1... Batch 4000 Discriminator Loss: 1.4037... Generator Loss: 0.7219
Epoch 1/1... Batch 4100 Discriminator Loss: 1.3813... Generator Loss: 0.7251
Epoch 1/1... Batch 4200 Discriminator Loss: 1.3836... Generator Loss: 0.7473
Epoch 1/1... Batch 4300 Discriminator Loss: 1.3911... Generator Loss: 0.7048
Epoch 1/1... Batch 4400 Discriminator Loss: 1.4221... Generator Loss: 0.6853
Epoch 1/1... Batch 4500 Discriminator Loss: 1.4089... Generator Loss: 0.7097
Epoch 1/1... Batch 4600 Discriminator Loss: 1.4030... Generator Loss: 0.6958
Epoch 1/1... Batch 4700 Discriminator Loss: 1.3952... Generator Loss: 0.7684
Epoch 1/1... Batch 4800 Discriminator Loss: 1.4072... Generator Loss: 0.6959
Epoch 1/1... Batch 4900 Discriminator Loss: 1.3834... Generator Loss: 0.7202
Epoch 1/1... Batch 5000 Discriminator Loss: 1.3828... Generator Loss: 0.7248
Epoch 1/1... Batch 5100 Discriminator Loss: 1.3784... Generator Loss: 0.7808
Epoch 1/1... Batch 5200 Discriminator Loss: 1.3880... Generator Loss: 0.7140
Epoch 1/1... Batch 5300 Discriminator Loss: 1.4225... Generator Loss: 0.6753
Epoch 1/1... Batch 5400 Discriminator Loss: 1.4151... Generator Loss: 0.6524
Epoch 1/1... Batch 5500 Discriminator Loss: 1.3846... Generator Loss: 0.6899
Epoch 1/1... Batch 5600 Discriminator Loss: 1.3793... Generator Loss: 0.7647
Epoch 1/1... Batch 5700 Discriminator Loss: 1.3900... Generator Loss: 0.7600
Epoch 1/1... Batch 5800 Discriminator Loss: 1.3718... Generator Loss: 0.7171
Epoch 1/1... Batch 5900 Discriminator Loss: 1.3531... Generator Loss: 0.7498
Epoch 1/1... Batch 6000 Discriminator Loss: 1.2757... Generator Loss: 0.8222
Epoch 1/1... Batch 6100 Discriminator Loss: 1.6193... Generator Loss: 0.6912
Epoch 1/1... Batch 6200 Discriminator Loss: 1.3775... Generator Loss: 0.6617
Epoch 1/1... Batch 6300 Discriminator Loss: 1.4854... Generator Loss: 0.5826

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.